Time-Varying Channel Estimation Based on Distributed Compressed Sensing for OFDM Systems.

For orthogonal frequency division multiplexing (OFDM) systems in high-mobility scenarios, the estimation of time-varying multipath channels not only has a large error, which affects system performance, but also requires plenty of pilots, resulting in low spectral efficiency. To address these issues, we propose a time-varying multipath channel estimation method based on distributed compressed sensing and a multi-symbol complex exponential basis expansion model (MS-CE-BEM) by exploiting the temporal correlation and the joint delay sparsity of wideband wireless channels within the duration of multiple OFDM symbols. Furthermore, in the proposed method, a sparse pilot pattern with the self-cancellation of pilot intercarrier interference (ICI) is adopted to reduce the input parameter error of the MS-CE-BEM, and a symmetrical extension technique is introduced to reduce the modeling error. Simulation results show that, compared with existing methods, this proposed method has superior performances in channel estimation and spectrum utilization for sparse time-varying channels.

Contemporary Epstein Criteria with Biopsy-Naïve Multiparametric Magnetic Resonance Imaging to Prevent Incorrect Assignment to Active Surveillance in the PI-RADS Version 2.0 Era.

PURPOSE: The aim of this study is to evaluate the effectiveness of multiparametric magnetic resonance imaging (mp-MRI) in prostate cancer (PCa) patients with biopsy Gleason score ≤ 6 who may otherwise be assigned to active surveillance (AS). PATIENTS AND METHODS: This was a retrospective study of 90 patients who underwent transrectal systematic biopsy for prostate cancer with Gleason score ≤ 6 without neoadjuvant therapy, with radical prostatectomy (RP) conducted between September 2009 and March 2018. All patients underwent prebiopsy mp-MRI. The prostate imaging reporting and data system (PI-RADS) version 2.0 score was evaluated. The correlation between imaging results and pathological findings was analyzed. We established models based on Epstein criteria with or without PI-RADS score and evaluated their ability for screening of potential PCa AS candidates. RESULTS: Among 90 patients, 60 (66.7%) had upgrade (Gleason ≥ 7), 30 (33.3%) had extraprostatic extension, and 9 (10%) had seminal vesicle invasion on RP specimens. The rate of unfavorable disease was 67.8% (61 of 90). On multivariate analysis, independent risk factors for unfavorable disease were prostate-specific antigen density and PI-RADS score. The model based on Epstein criteria with PI-RADS score showed improved integrated discrimination improvement index and was superior to the classical Epstein criteria on decision curve analysis for screening potential prostate cancer AS candidates. CONCLUSIONS: Multiparametric MRI with PIRADS 2.0 provides useful supplementary information to Epstein criteria, and may prevent incorrect assignment to active surveillance.

Establishment of a multi-parameters MRI model for predicting small lymph nodes metastases (<10 mm) in patients with resected pancreatic ductal adenocarcinoma.

PURPOSE: To evaluate the potential role of MR findings and DWI parameters in predicting small regional lymph nodes metastases (with short-axis diameter < 10 mm) in pancreatic ductal adenocarcinomas (PDACs). METHODS: A total of 127 patients, 82 in training group and 45 in testing group, with histopathologically diagnosed PDACs who underwent pancreatectomy were retrospectively analyzed. PDACs were divided into two groups of positive and negative lymph node metastases (LNM) based on the pathological results. Pancreatic cancer characteristics, short axis of largest lymph node, and DWI parameters of PDACs were evaluated. RESULTS: Univariate and multivariate analyses showed that extrapancreatic distance of tumor invasion, short-axis diameter of the largest lymph node, and mean diffusivity of tumor were independently associated with small LNM in patients with PDACs. The combining MRI diagnostic model yielded AUCs of 0.836 and 0.873, and accuracies of 81.7% and 80% in the training and testing groups. The AUC of the MRI model for predicting LNM was higher than that of subjective MRI diagnosis in the training group (rater 1, P = 0.01; rater 2, 0.008) and in a testing group (rater 1, P = 0.036; rater 2, 0.024). Comparing the subjective diagnosis, the error rate of the MRI model was decreased. The defined LNM-positive group by the MRI model showed significantly inferior overall survival compared to the negative group (P = 0.006). CONCLUSIONS: The MRI model showed excellent performance for individualized and noninvasive prediction of small regional LNM in PDACs. It may be used to identify PDACs with small LNM and contribute to determining an appropriate treatment strategy for PDACs.

PI-RADS v2 and periprostatic fat measured on multiparametric magnetic resonance imaging can predict upgrading in radical prostatectomy pathology amongst patients with biopsy Gleason score 3 + 3 prostate cancer.

PURPOSE: An underestimated biopsy Gleason score 3 + 3 can result in unfounded optimism amongst patients and cause physicians to miss the window for prostate cancer (PCa) cure. This study aims to evaluate the effectiveness of Prostate Imaging Reporting and Data System (PI-RADS) version 2 as well as periprostatic fat (PPF) measured on multiparametric magnetic resonance imaging (mp-MRI) at predicting pathological upgrading amongst patients with biopsy Gleason score 3 + 3 disease. PATIENTS AND METHODS: A retrospective analysis of 56 patients with biopsy Gleason score 6 PCa who underwent prebiopsy mp-MRI and radical prostatectomy (RP) between November 2013 and March 2018 was conducted. Two radiologists performed PI-RADS v2 score evaluation and different fat measurements on mp-MRI. The associations amongst clinical information, PI-RADS v2 score, different fat parameters and pathologic findings were analyzed. A nomogram predicting upgrading was established based on the results of logistic regression analysis. RESULTS: A total of 38 (67.9%) patients were upgraded to Gleason ≥7 disease on RP specimens. Prostate-specific antigen density (PSAD) (p < .001), positive core (p < .001), single-core positivity (p = .039), PI-RADS score (p < .001), front PPF area (p = .007) and front-to-total ratio (the ratio of front PPF area to total contour area) (p < .001) were risk factors for upgrading. On multivariate analysis, Epstein criteria (p = .02), PI-RADS score >3 (p = .024), and front-to-total ratio (p = .006) were independent risk factors for pathologic upgrading. The AUC value of the nomogram was 0.893 (95% CI, 0.787-0.999). CONCLUSION: The combination of PI-RADS v2 and periprostatic fat measured on mp-MRI can help predict pathologic upgrading amongst patients with biopsy Gleason score 3 + 3 PCa.